--- title: OCO2 - Reproduce F. Chevallier's figures keywords: fastai sidebar: home_sidebar ---
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{% endraw %}

Open In Colab

{% raw %} 
!apt-get install libgeos-3.5.0
!apt-get install libgeos-dev
!pip install https://github.com/matplotlib/basemap/archive/master.zip
{% endraw %} {% raw %} 
from google.colab import drive
drive.mount('/content/drive')
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Introduction

Using Data from OCO-2 Satellite, issued by the NASA.

We here try to reproduce the results from this paper by F. Chevallier, trying to "[observe] carbon dioxide emissions over China's cities with the Orbiting Carbon Observatory-2".

//TODO: Explanation

{% raw %} 
import pandas as pd
import numpy as np
from numpy import exp, loadtxt, pi, sqrt
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.basemap import Basemap  #Imported directly from the github repository
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Show Data on the map

draw_map: Function to draw the map and the observations (relief style). The column names can be specified in the arguments.

Parameters:

  • (DataFrame) data: the dataset to map.
  • (string) x : the name of the longitude column. default: 'longitide'
  • (string) y: the name of the latitude column. default: 'latitude'
  • (string) c: the name of the XCO2 column (or other measure wanted to be plotted). default: 'xco2'
  • (int) lon_min : the minimum longitude. default: -180
  • (int) lon_max: the maximum longitude. default: 180
  • (int) lat_min: the minimum latitude. default: -90
  • (int) lat_max: the maximum latitude. default: 90
  • (int) size_point: size of the point to plot (useful if we zoom in). default: 1
  • (Bool) frontier: whether or not to draw the countries borders. default: False
{% raw %} 
def draw_map(data, x="longitude", y="latitude", c="xco2", lon_min=-180, lon_max=180, lat_min=-90, lat_max=90, size_point=1, frontier=False):

    plt.figure(figsize=(15, 10), edgecolor='w')
    m = Basemap(llcrnrlat=lat_min, urcrnrlat=lat_max, llcrnrlon=lon_min, urcrnrlon=lon_max)
    
    m.shadedrelief()
    
    parallels = np.arange(-80.,81,10.)
    m.drawparallels(parallels,labels=[False,True,True,False])

    meridians = np.arange(10.,351.,20.)
    m.drawmeridians(meridians,labels=[True,False,False,True])

    normal = matplotlib.colors.LogNorm(vmin=data[c].min(), vmax=data[c].max())

    m.scatter(data[x], data[y], c=data[c], cmap=plt.cm.jet, s=size_point, norm=normal)

    if (frontier):
      m.drawcountries(linewidth=0.5)
      m.drawcoastlines(linewidth=0.7)

    plt.show()
{% endraw %}
haversine: Function to calculate the great circle distance between two points on the earth given longitude and latitude.

Parameters:

  • (int) lon1 : the longitude of the first point.
  • (int) lon2: the latitude of the first point.
  • (int) lat1: the longitude of the second point.
  • (int) lat2: the latitude of the second point.

Return:

  • (int) the distance (in kilometers)
{% raw %} 
from math import radians, cos, sin, asin, sqrt

def haversine_formula(lon1, lat1, lon2, lat2):
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # Radius of earth in kilometers. Use 3956 for miles
    return c * r

def haversine(row, lat, lon):
    return haversine_formula(lon, lat, row['longitude'], row['latitude'])
{% endraw %}

Retieve Data

Sample data can be accessed freely on the NASA Database, among other open data from several NASA sattelites.

We will be using a CSV of peaks detected by Benoit Courty here.

{% raw %} 
data = pd.read_csv("https://courty.fr/OCO2/result_doco2_1808-o22158.csv", sep=",", index_col=0)

data.head()
sounding_id latitude longitude orbit slope intercept amplitude sigma delta R windspeed_u windspeed_v
0 2018080101035604 25.425072 -177.345490 21709 -0.000017 404.786068 -9.322890 -3.818291 0.974073 0.660388 -7.759225 -0.739198
1 2018080101050203 29.112371 -178.352524 21709 -0.001326 405.023519 2.480614 2.097535 0.471802 0.651822 -6.748729 -1.210935
2 2018080101062177 33.494995 -179.648438 21709 -0.002496 404.507099 6.427538 2.924886 0.876690 0.698739 -4.581494 -0.911555
3 2018080102464472 38.513145 153.990067 21710 -0.002990 404.778809 -8.891458 -2.091188 1.696251 0.686102 -3.375677 8.726022
4 2018080102564438 69.815544 131.086807 21710 -0.008529 399.893348 6.977613 2.478258 1.123234 0.517606 -3.290220 -0.373389
{% endraw %} {% raw %} 
data.describe()
sounding_id latitude longitude orbit slope intercept amplitude sigma delta R windspeed_u windspeed_v
count 2.313000e+03 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000
mean 2.018082e+15 20.868098 -2.349540 21946.058366 -0.002569 404.189876 18.745080 14.348495 0.536493 0.588711 -1.249908 -0.063434
std 9.199090e+08 25.944414 107.720979 134.152974 0.009088 1.985969 23.779654 12.464226 0.434811 0.076865 4.430765 3.591642
min 2.018080e+15 -36.018448 -179.648438 21709.000000 -0.070105 398.965334 -122.489640 -33.132299 0.003814 0.500004 -9.020993 -13.380523
25% 2.018081e+15 -2.046461 -112.551079 21814.000000 -0.004114 402.884122 4.937479 7.606247 0.233205 0.529653 -4.993138 -2.770149
50% 2.018082e+15 30.276360 15.994817 21963.000000 -0.002153 404.599571 13.087726 15.043082 0.439802 0.564811 -2.178347 -0.072568
75% 2.018083e+15 39.875168 81.461533 22061.000000 0.001393 405.883681 26.872900 23.688184 0.716917 0.629732 2.718249 2.928468
max 2.018083e+15 72.814903 179.976379 22158.000000 0.017307 408.109593 221.239819 33.288264 3.943475 0.924756 13.018653 11.974917
{% endraw %}

We spot some negative sigma and amplitude parameters.

{% raw %} 
data['sigma'] = data['sigma'].apply(abs)
data['amplitude'] = data['amplitude'].apply(abs)
data.head(15)
sounding_id latitude longitude orbit slope intercept amplitude sigma delta R windspeed_u windspeed_v
0 2018080101035604 25.425072 -177.345490 21709 -0.000017 404.786068 9.322890 3.818291 0.974073 0.660388 -7.759225 -0.739198
1 2018080101050203 29.112371 -178.352524 21709 -0.001326 405.023519 2.480614 2.097535 0.471802 0.651822 -6.748729 -1.210935
2 2018080101062177 33.494995 -179.648438 21709 -0.002496 404.507099 6.427538 2.924886 0.876690 0.698739 -4.581494 -0.911555
3 2018080102464472 38.513145 153.990067 21710 -0.002990 404.778809 8.891458 2.091188 1.696251 0.686102 -3.375677 8.726022
4 2018080102564438 69.815544 131.086807 21710 -0.008529 399.893348 6.977613 2.478258 1.123234 0.517606 -3.290220 -0.373389
5 2018080104220301 26.624556 132.902069 21711 -0.002916 406.237532 69.440162 22.553555 1.228304 0.602475 3.076021 5.960368
6 2018080104241836 34.093071 130.723083 21711 -0.001673 405.832664 7.668979 3.101237 0.986535 0.564589 -4.438503 -0.151730
7 2018080104243337 34.913078 130.466461 21711 -0.003367 405.669386 8.073160 3.172612 1.015165 0.712848 -2.819998 -2.047116
8 2018080104244906 35.789249 130.185776 21711 -0.004689 405.292668 8.141250 8.207207 0.395736 0.515282 -2.545387 -1.840344
9 2018080104245535 36.149014 130.068329 21711 -0.004583 405.161842 2.956341 12.244524 0.096321 0.662136 -2.414776 -1.631612
10 2018080109202134 33.142223 58.612873 21714 -0.003582 405.647000 16.558232 25.105055 0.263125 0.567559 -4.593277 -6.853858
11 2018080109202172 33.181385 58.603271 21714 -0.003824 405.663111 12.688570 19.986778 0.253268 0.633864 -4.626702 -6.725498
12 2018080109202232 33.221443 58.592304 21714 -0.003679 405.641462 12.671835 17.378246 0.290900 0.575524 -4.656679 -6.593145
13 2018080109202377 33.253674 58.580158 21714 -0.003691 405.624335 12.608016 15.629924 0.321810 0.542875 -4.671719 -6.482388
14 2018080109202436 33.303322 58.567360 21714 -0.003577 405.611281 11.400622 12.214116 0.372372 0.529896 -4.706113 -6.321278
{% endraw %} {% raw %} 
data.describe()
sounding_id latitude longitude orbit slope intercept amplitude sigma delta R windspeed_u windspeed_v
count 2.313000e+03 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000 2313.000000
mean 2.018082e+15 20.868098 -2.349540 21946.058366 -0.002569 404.189876 20.951823 16.640968 0.536493 0.588711 -1.249908 -0.063434
std 9.199090e+08 25.944414 107.720979 134.152974 0.009088 1.985969 21.859396 9.180615 0.434811 0.076865 4.430765 3.591642
min 2.018080e+15 -36.018448 -179.648438 21709.000000 -0.070105 398.965334 0.100808 2.000477 0.003814 0.500004 -9.020993 -13.380523
25% 2.018081e+15 -2.046461 -112.551079 21814.000000 -0.004114 402.884122 6.402464 8.815902 0.233205 0.529653 -4.993138 -2.770149
50% 2.018082e+15 30.276360 15.994817 21963.000000 -0.002153 404.599571 14.047673 16.074852 0.439802 0.564811 -2.178347 -0.072568
75% 2.018083e+15 39.875168 81.461533 22061.000000 0.001393 405.883681 27.590997 24.291017 0.716917 0.629732 2.718249 2.928468
max 2.018083e+15 72.814903 179.976379 22158.000000 0.017307 408.109593 221.239819 33.288264 3.943475 0.924756 13.018653 11.974917
{% endraw %}

To convert the sounding_id into a datetime variable data:

{% raw %} 
from datetime import datetime
def to_date(a):
    return datetime.strptime(str(a), '%Y%m%d%H%M%S%f')

data['date'] = data['sounding_id'].apply(to_date)
data.head()
sounding_id latitude longitude orbit slope intercept amplitude sigma delta R windspeed_u windspeed_v date
0 2018080101035604 25.425072 -177.345490 21709 -0.000017 404.786068 9.322890 3.818291 0.974073 0.660388 -7.759225 -0.739198 2018-08-01 01:03:56.040
1 2018080101050203 29.112371 -178.352524 21709 -0.001326 405.023519 2.480614 2.097535 0.471802 0.651822 -6.748729 -1.210935 2018-08-01 01:05:02.030
2 2018080101062177 33.494995 -179.648438 21709 -0.002496 404.507099 6.427538 2.924886 0.876690 0.698739 -4.581494 -0.911555 2018-08-01 01:06:21.770
3 2018080102464472 38.513145 153.990067 21710 -0.002990 404.778809 8.891458 2.091188 1.696251 0.686102 -3.375677 8.726022 2018-08-01 02:46:44.720
4 2018080102564438 69.815544 131.086807 21710 -0.008529 399.893348 6.977613 2.478258 1.123234 0.517606 -3.290220 -0.373389 2018-08-01 02:56:44.380
{% endraw %}

Plot Peaks

Simple plot (Gaussian)

{% raw %} 
import scipy.stats as stats

df = data[:5]

for sigma in df['sigma'][:5]:

    std = abs(sigma)

    x = np.linspace(-15, 15, 100)
    y = stats.norm.pdf(x,0,std)

    plt.plot(x,y, color='coral')

    plt.grid()

    plt.show()
{% endraw %}

Map plot

{% raw %} 
from plotly.figure_factory import create_quiver
import plotly.graph_objects as go
import plotly.express as px

fig = px.scatter_mapbox(data, lat="latitude", lon="longitude", 
                        hover_name="date", hover_data=["amplitude", "sigma"],
                        color_discrete_sequence=["red"], zoom=2, height=700)

fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

fig.show()
fig.write_html("./peaks.html")
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Map plot (Folium)

{% raw %} 
import folium

folium_map = folium.Map([43, -100], zoom_start=4,tiles="CartoDB dark_matter")

for index, row in df_res.iterrows():
    radius = row["amplitude"]/20
    color="#E37222" # tangerine
    tooltip =  "["+str(round(row['latitude'],2))+" ; "+str(round(row['longitude'],2))+"]"
    
    folium.CircleMarker(location=(row["latitude"],
                                  row["longitude"]),
                        radius=radius,
                        color=color,
                        tooltip=tooltip,
                        fill=True).add_to(folium_map)

folium_map
{% endraw %}

Map Plot woth Scatter Plots (INCOMPLETE)

{% raw %} 
!pip install geopandas
{% endraw %} {% raw %} 
import os
import glob
import numpy as np
import pandas as pd

input_dir = 'http://courty.fr/OCO2/'
{% endraw %} {% raw %} 
# http://courty.fr/OCO2/peak_data-f_oco2_1808-o_21733-si_2018080216413233.json

def load_one_peak_data(sounding_id, df_all_peak):
    df_param = df_all_peak.query("sounding_id==@sounding_id")
    param_index = df_param.index[0]
    url = input_dir + "peak_data-f_oco2_1808-o_"+str(df_param.loc[param_index, 'orbit'])+"-si_"+sounding_id+".json"
    print(url)
    df_peak = pd.read_json(url)
    
    gaussian_param = {
        'slope' : df_param.loc[param_index, 'slope'],
        'intercept' : df_param.loc[param_index, 'intercept'],
        'amplitude' : df_param.loc[param_index, 'amplitude'],
        'sigma': df_param.loc[param_index, 'sigma'],
        'delta': df_param.loc[param_index, 'delta'],
        'R' : df_param.loc[param_index, 'R'],
    }
    return df_peak, gaussian_param

'''
x : the data input value
m : the slope of the data
b : the intercep of the data
A : Amplitude de la courbe
sig : sigma / écart type de la courbe
'''
def gaussian(x, m, b, A, sig):
    return m * x + b + A / (sig * (2 * np.pi)**0.5) * np.exp(-x**2 / (2*sig**2))

def plot_peak(df_peak, gaussian_param):
    x = df_peak['distance']
    y = df_peak['xco2']
    y = y - gaussian_param['slope'] * x - gaussian_param['intercept']
    plt.scatter(x, y, c=y, s=3, label='sounding')
    plt.plot(x, gaussian(x, m=0, b=0, A=gaussian_param['amplitude'], sig=gaussian_param['sigma']), 'r', label='fit')
    plt.legend()
    plt.title('OCO 2 data')
    plt.xlabel('Distance')
    plt.ylabel('CO²')
    plt.show()
{% endraw %} {% raw %} 
df_res = pd.read_csv(input_dir + "result_for_oco2_1808.csv", sep=",")
df_peak, gaussian_param = load_one_peak_data("2018083122324108", df_res)
print(gaussian_param)
plot_peak(df_peak, gaussian_param)

http://courty.fr/OCO2/peak_data-f_oco2_1808-o_22159-si_2018083122324108.json
{'slope': 0.004776745630485172, 'intercept': 406.0322138728357, 'amplitude': 7.993881983969049, 'sigma': 20.035509511802843, 'delta': 0.15917226891913366, 'R': 0.5012533577553441}
{% endraw %} {% raw %} 
import folium

folium_map = folium.Map([43, -100], zoom_start=4,tiles="CartoDB dark_matter")

for index, row in df_res.iterrows():
    radius = row["amplitude"]/20
    color="#E37222" # tangerine
    tooltip =  "["+str(round(row['latitude'],2))+" ; "+str(round(row['longitude'],2))+"]"
    # popup = folium.Popup(max_width=450).add_child(folium.Vega(peak, width=450, height=250))

    # url = input_dir + "peak_data-f_oco2_1808-o_"+str(int(row['orbit']))+"-si_"+str(int(row['sounding_id']))+".json"
    # print(url)
    # peak = pd.read_json(url)
    
    folium.CircleMarker(location=(row["latitude"],
                                  row["longitude"]),
                        radius=radius,
                        color=color,
                        tooltip=tooltip,
                        # popup=popup,
                        fill=True).add_to(folium_map)

folium_map
{% endraw %}

Map plot with wind vectors (INCOMPLETE)

{% raw %} 
# Create quiver figure
fig = create_quiver(data.longitude, data.latitude, data.windspeed_u, data.windspeed_v,
                       scale=.25,
                       arrow_scale=.4,
                       name='quiver',
                       line_width=1)

fig.add_trace(go.Scatter(x=data.longitude, y=data.latitude,
                    mode='markers',
                    marker_size=2,
                    name='points'))


fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})

fig.show()
fig.write_html("./winds.html")
{% endraw %}

Map Plot with EDGAR sources (INCOMPLETE - Too much data)

{% raw %} 
path_Edgar_2018 = "/content/drive/My Drive/Data For Good - S7: Ministère & O-CO2/data/edgar/CO2_emissions_Edgar_2018_v3.csv"

edgar = pd.read_csv(path_Edgar_2018, sep=",", index_col=0)

edgar.head()

/usr/local/lib/python3.6/dist-packages/numpy/lib/arraysetops.py:569: FutureWarning:

elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
latitude longitude CO2 emissions CO2 classification CO2 classification encoded
0 -89.949997 0.050000 0.0 Low 0
1 -78.349998 -147.949997 0.0 Low 0
2 -78.449997 -147.949997 0.0 Low 0
3 -78.550003 -147.949997 0.0 Low 0
4 -78.650002 -147.949997 0.0 Low 0
{% endraw %} {% raw %} 
# We only keep the 75% highest registered emitters
edgar_top = edgar[edgar['CO2 classification encoded'] > 2]
edgar_top.describe()

edgar_top['CO2 classification encoded'][round(0.95*dataframe_list_v1.shape[0]):] = 'Very high'
latitude longitude CO2 emissions CO2 classification encoded
count 324000.000000 324000.000000 3.240000e+05 324000.0
mean 31.408045 10.237668 3.633424e-08 3.0
std 22.213159 83.376957 4.110545e-07 0.0
min -77.849998 -179.949997 2.126474e-09 3.0
25% 24.450001 -79.049988 3.597865e-09 3.0
50% 36.750000 19.750000 6.291224e-09 3.0
75% 45.849998 82.150002 1.361606e-08 3.0
max 80.050003 179.949997 1.208277e-04 3.0
{% endraw %} {% raw %} 
folium_map = folium.Map([43, -100], zoom_start=4,tiles="CartoDB dark_matter")

for index, row in df_res.iterrows():
    radius = row["amplitude"]/20
    color="#E37222" # tangerine
    tooltip =  "["+str(round(row['latitude'],2))+" ; "+str(round(row['longitude'],2))+"]"
    # popup = folium.Popup(max_width=450).add_child(folium.Vega(peak, width=450, height=250))

    # url = input_dir + "peak_data-f_oco2_1808-o_"+str(int(row['orbit']))+"-si_"+str(int(row['sounding_id']))+".json"
    # print(url)
    # peak = pd.read_json(url)
    
    folium.CircleMarker(location=(row["latitude"],
                                  row["longitude"]),
                        radius=radius,
                        color=color,
                        tooltip=tooltip,
                        # popup=popup,
                        fill=True).add_to(folium_map)
    
for index, row in edgar_top.iterrows():
    radius = 1
    color="#3186CC" # blue
    tooltip =  "["+str(round(row['latitude'],2))+" ; "+str(round(row['longitude'],2))+"]"
    
    folium.CircleMarker(location=(row["latitude"],
                                  row["longitude"]),
                        radius=radius,
                        color=color,
                        tooltip=tooltip,
                        fill=True).add_to(folium_map)

folium_map
{% endraw %}

Map plot with Centrale sources

{% raw %} 
path_plants = "https://raw.githubusercontent.com/dataforgoodfr/batch7_satellite_ges/master/dataset/CO2_emissions_centrale.csv"

plants = pd.read_csv(path_plants, sep=",", index_col=0)

plants.head()
country_long capacity_mw latitude longitude primary_fuel generation_gwh_2013 generation_gwh_2014 generation_gwh_2015 generation_gwh_2016 generation_gwh_2017 estimated_generation_gwh gCO2/KWh generation_gwh_2017_with_estimated_data tCO2_emitted_in_2013 tCO2_emitted_in_2014 tCO2_emitted_in_2015 tCO2_emitted_in_2016 tCO2_emitted_in_2017 tCO2_emitted_in_2017_with estimated_data
0 Afghanistan 42.0 34.5638 69.1134 Gas NaN NaN NaN NaN NaN NaN 443 NaN NaN NaN NaN NaN NaN NaN
1 Algeria 520.0 35.8665 6.0262 Gas NaN NaN NaN NaN NaN 2152.249819 443 2152.249819 NaN NaN NaN NaN NaN 9.534467e+05
2 Algeria 71.0 36.8924 7.7634 Gas NaN NaN NaN NaN NaN 293.864879 443 293.864879 NaN NaN NaN NaN NaN 1.301821e+05
3 Algeria 560.0 36.5988 3.1375 Gas NaN NaN NaN NaN NaN 2317.807497 443 2317.807497 NaN NaN NaN NaN NaN 1.026789e+06
4 Algeria 100.0 36.5914 2.9223 Gas NaN NaN NaN NaN NaN 413.894196 443 413.894196 NaN NaN NaN NaN NaN 1.833551e+05
{% endraw %} {% raw %} 
plants.describe()
capacity_mw latitude longitude generation_gwh_2013 generation_gwh_2014 generation_gwh_2015 generation_gwh_2016 generation_gwh_2017 estimated_generation_gwh gCO2/KWh generation_gwh_2017_with_estimated_data tCO2_emitted_in_2013 tCO2_emitted_in_2014 tCO2_emitted_in_2015 tCO2_emitted_in_2016 tCO2_emitted_in_2017 tCO2_emitted_in_2017_with estimated_data
count 8602.000000 8602.000000 8602.000000 3078.000000 3117.000000 3312.000000 3274.000000 3210.000000 5403.000000 8602.000000 8440.000000 3.078000e+03 3.117000e+03 3.312000e+03 3.274000e+03 3.210000e+03 8.440000e+03
mean 434.151369 27.287221 -5.885974 1045.620597 1091.104519 1120.312133 1058.376820 1134.772571 2265.492359 700.833062 1842.772024 8.687061e+05 9.019768e+05 9.131356e+05 8.718276e+05 9.289991e+05 1.538757e+06
std 690.388169 24.641181 86.714480 2578.122419 2715.041407 2675.889462 2632.241272 2674.701678 7140.625909 256.469725 5953.860708 2.528086e+06 2.625053e+06 2.607140e+06 2.559000e+06 2.588440e+06 6.046563e+06
min 1.000000 -77.847000 -179.977700 -2.653000 -262.902000 -141.014000 -127.507000 -132.033000 0.000000 443.000000 -132.033000 -1.845416e+03 -2.045378e+05 -6.246920e+04 -5.648560e+04 -5.849062e+04 -5.849062e+04
25% 16.073750 21.434975 -84.425525 0.035250 0.032000 0.025000 0.019000 0.341514 89.494709 443.000000 18.882956 2.567400e+01 2.134832e+01 1.711600e+01 1.287562e+01 2.172565e+02 1.086195e+04
50% 132.250000 35.594300 -23.547500 18.245000 16.227000 22.749000 16.377500 36.623000 737.318085 778.000000 340.484944 8.859949e+03 8.021180e+03 1.098873e+04 8.077698e+03 1.881502e+04 2.080822e+05
75% 600.000000 42.104150 79.832125 633.405479 630.858000 762.341497 583.308501 697.294500 2782.119122 1050.000000 2240.154958 3.416080e+05 3.368590e+05 4.247199e+05 3.279560e+05 3.801258e+05 1.423369e+06
max 8865.000000 71.292000 178.835900 27586.200000 28127.000000 32157.000000 30015.000000 35116.000000 450562.692350 1050.000000 450562.692350 2.896551e+07 2.953335e+07 3.376485e+07 3.151575e+07 3.687180e+07 4.730908e+08
{% endraw %} {% raw %} 
peaks_and_plants_dark = folium.Map([43, 0], zoom_start=4,tiles="CartoDB dark_matter")

for index, row in plants.iterrows():
    radius = 0.1
    color="#3186CC" # blue
    tooltip =  "["+str(round(row['latitude'],2))+" ; "+str(round(row['longitude'],2))+"]"
    emit = str(round(row['estimated_generation_gwh'],2))
    popup_html="""<h4>"""+tooltip+"""</h4>"""+row['country_long']+"""<p>Emission 2018 (est): """+emit+""" GWh</p>"""

    popup=folium.Popup(popup_html, max_width=450)

    folium.CircleMarker(location=(row["latitude"],
                                  row["longitude"]),
                        radius=radius,
                        color=color,
                        tooltip=tooltip,
                        popup=popup,
                        fill=True).add_to(peaks_and_plants_dark)

for index, row in data.iterrows():
    radius = row["amplitude"]/20
    color="#E37222" # tangerine
    tooltip =  "["+str(round(row['latitude'],2))+" ; "+str(round(row['longitude'],2))+"]"
    sounding = str(row['sounding_id'])
    date = str(row['date'])
    orbit = str(row['orbit'])
    
    popup_html="""<h4>"""+tooltip+"""</h4>"""+date+"""<p>sounding_id: """+sounding+"""</br>orbit: """+orbit+"""</p>"""

    popup=folium.Popup(popup_html, max_width=450)
    
    folium.CircleMarker(location=(row["latitude"],
                                  row["longitude"]),
                        radius=radius,
                        color=color,
                        tooltip=tooltip,
                        popup=popup,
                        fill=True).add_to(peaks_and_plants_dark)

peaks_and_plants_dark
{% endraw %} {% raw %} 
peaks_and_plants.save('peaks_and_plants.html')
{% endraw %}